Rain Gage

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Robert K. Crane - One of the best experts on this subject based on the ideXlab platform.

  • space time structure of Rain rate fields
    Journal of Geophysical Research, 1990
    Co-Authors: Robert K. Crane
    Abstract:

    Information on the spatial and temporal statistics of Rain rate is needed for the design of remote sensing systems for the measurement of areal Rainfall accumulation and for the design of millimeter wave communication systems. In this study, Rain Gage and radar data were used to determine empirically the spatial and temporal structure of the Rain process as observed using Rain rate as a tracer of the atmospheric motions and to test the validity of Taylor's hypothesis for relating their spatial and temporal statistics. Weather radar derived Rain rate maps were employed to obtain one- and two-dimensional spatial power spectra. Azimuthally averaged two-dimensional spectra displayed the shape predicted for a passive scalar advected by a steady state field of two-dimensional turbulence driven by the input of energy over a narrow band of wave numbers. One-dimensional spatial spectra for a short line of Rain Gages had the same spectral shape as the azimuthally averaged spectra obtained from the radar data. Temporal spectra from the Gage time series were nearly identical in shape to the one-dimensional spatial spectra if less than a half hour of data were processed to generate a spectrum and a constant translation velocity was assumed to relate themore » temporal and spatial scales. For spectra corresponding to longer durations, a match could not be made.« less

  • Space‐time structure of Rain rate fields
    Journal of Geophysical Research, 1990
    Co-Authors: Robert K. Crane
    Abstract:

    Information on the spatial and temporal statistics of Rain rate is needed for the design of remote sensing systems for the measurement of areal Rainfall accumulation and for the design of millimeter wave communication systems. In this study, Rain Gage and radar data were used to determine empirically the spatial and temporal structure of the Rain process as observed using Rain rate as a tracer of the atmospheric motions and to test the validity of Taylor's hypothesis for relating their spatial and temporal statistics. Weather radar derived Rain rate maps were employed to obtain one- and two-dimensional spatial power spectra. Azimuthally averaged two-dimensional spectra displayed the shape predicted for a passive scalar advected by a steady state field of two-dimensional turbulence driven by the input of energy over a narrow band of wave numbers. One-dimensional spatial spectra for a short line of Rain Gages had the same spectral shape as the azimuthally averaged spectra obtained from the radar data. Temporal spectra from the Gage time series were nearly identical in shape to the one-dimensional spatial spectra if less than a half hour of data were processed to generate a spectrum and a constant translation velocity was assumed to relate themore » temporal and spatial scales. For spectra corresponding to longer durations, a match could not be made.« less

Barbara Minsker - One of the best experts on this subject based on the ideXlab platform.

  • eScience - Virtual Sensors in a Web 2.0 Virtual Watershed
    2008 IEEE Fourth International Conference on eScience, 2008
    Co-Authors: Luigi Marini, Rob Kooper, Alejandro Rodriguez, David J. Hill, James D. Myers, Barbara Minsker
    Abstract:

    This paper presents a Web 2.0 virtual observatory framework applied in an environmental watershed research context, where users not only can access existing sensor data such as the USGS (United States Geological Survey) Rain Gage data in near real-time, but also can create and share virtual sensors and trigger their associated workflows on-the-fly. Categories of virtual sensors are discussed and community participation and collaboration on creating virtual sensors can be promoted. An eScience use case which allows users to create virtual Rain Gages on a Google map front end using NEXRAD (next generation weather radar) data is presented.

  • Virtual Sensors in a Web 2.0 Virtual Watershed
    2008 IEEE Fourth International Conference on eScience, 2008
    Co-Authors: Luigi Marini, Rob Kooper, Alejandro Rodriguez, David Hill, James Myers, Barbara Minsker
    Abstract:

    This paper presents a Web 2.0 virtual observatory framework applied in an environmental watershed research context, where users not only can access existing sensor data such as the USGS (United States Geological Survey) Rain Gage data in near real-time, but also can create and share virtual sensors and trigger their associated workflows on-the-fly. Categories of virtual sensors are discussed and community participation and collaboration on creating virtual sensors can be promoted. An eScience use case which allows users to create virtual Rain Gages on a Google map front end using NEXRAD (next generation weather radar) data is presented.

David C Curtis - One of the best experts on this subject based on the ideXlab platform.

  • Evaluation of the Spatial Structure of Storms and the Development of Design Storms
    World Environmental and Water Resources Congress 2007, 2007
    Co-Authors: David C Curtis
    Abstract:

    Traditional analyses defining the spatial structure of design storms are interpreted from point Rain Gage data. However, rarely are Rain Gage networks dense enough to provide accurate assessments of the very storms these networks are intended to monitor. Furthermore, Rainfall topologies interpolated from point data may be quite different from the actual storm shapes. For the past decade, large scale databases of radar Rainfall estimates have been created and archived providing a rich store of info rmation that has never existed before. The se databases can be mined for detailed information regarding the spatial characteristics of storms. This paper will present findings on the spatial structure of storms from Florida and other locations in the United States. Example frequency distribution s of storm parameters will be presented. In addition, information will be presented on how th ese data are being used to develop more realistic design storms.

  • Creating a Seamless Map of Gage-Adjusted Radar Rainfall Estimates for the State of Florida
    World Water & Environmental Resources Congress 2003, 2003
    Co-Authors: Brian C. Hoblit, Cris Castello, David C Curtis
    Abstract:

    Rainfall distributions from Rain Gages are typically estimated by assuming a spatial geometry tied to point Rain Gage observations using, for example, Thiessen polygons, inverse distance squared weighting, or statistical Kriging techniques. Unfortunately, the spatial distributions inferred by these approaches have little connection with how Rain actually falls. Since the release of the WSR-88D (NEXRAD) radar in the early 1990s, many hydrologists and engineers have begun using Gage-adjusted radar Rainfall estimates for hydrologic and water resource modeling. Over large areas under multiple NEXRAD radar coverages, the quality of radar Rainfall estimates can vary significantly from one location to another. Visible discontinuities can develop at the limits of coverage of a single NEXRAD site because of slightly different performance or calibration techniques used at the different radar sites. Using a variety of GIS procedures for this study, these discontinuities were eliminated and locations of ground clutter were suppressed, yielding a seamless map of unadjusted radar Rainfall estimates. These data were adjusted with over 400 Rain Gages located throughout the state using a modified spatial adjustment technique originally developed by Brandes at the National Severe Storms Lab in the mid-1970s. This approach was able to retain the volumetric Rainfall estimates from the Gages while maintaining the spatial signature of the Rainfall. Use of this technique greatly improves Gage-adjusted radar Rainfall estimates.

  • Comparing Spatial Distributions of Rainfall Derived from Rain Gages and Radar 1
    1999
    Co-Authors: David C Curtis, Brett S. Clyde
    Abstract:

    Traditional Rainfall analyses for hydrologic modeling use spatial representations of Rainfall derived from Rain Gage observations at a series of points. These Gage-derived spatial representations of Rainfall are computed using any number of techniques including inverse-distance squared weighting and more advanced methods such as Kriging. None of these techniques have any relationship with the real world or provide any information about the true spatial distribution of Rainfall. They are simply methods of convenience used to interpret the spatial variation of Rainfall from point data in the absence of other information or techniques. Radar, on the other hand, offers a significant analytical improvement for Rainfall analysis by providing direct data more representative of the true spatial distribution of Rainfall. The differences between the spatial distributions derived from radar and those derived from Rain Gages are often striking and dramatic. Examples will be presented where the contours indicating general spatial trends are rotated nearly 90 degrees. These findings have significant implications for both modeling and for hydrologic standards that require data supporting design storm shapes and sizes.

  • Rain Gage network size for automated flood warning systems
    Engineering Hydrology, 1993
    Co-Authors: David C Curtis, Harry W Dotson
    Abstract:

    A methodology is presented that establishes a rational framework for estimating the optimum Rain Gage network size for flash flood warning systems based on network performance and economic criteria. Network performance is measured statistically by the estimated coefficient of variation of mean areal Rainfall. Rain Gage network performance is used as a surrogate to estimate the performance of the flood forecasting system in generating flood warning benefits (i.e. potential damage reduction).

Luigi Marini - One of the best experts on this subject based on the ideXlab platform.

  • eScience - Virtual Sensors in a Web 2.0 Virtual Watershed
    2008 IEEE Fourth International Conference on eScience, 2008
    Co-Authors: Luigi Marini, Rob Kooper, Alejandro Rodriguez, David J. Hill, James D. Myers, Barbara Minsker
    Abstract:

    This paper presents a Web 2.0 virtual observatory framework applied in an environmental watershed research context, where users not only can access existing sensor data such as the USGS (United States Geological Survey) Rain Gage data in near real-time, but also can create and share virtual sensors and trigger their associated workflows on-the-fly. Categories of virtual sensors are discussed and community participation and collaboration on creating virtual sensors can be promoted. An eScience use case which allows users to create virtual Rain Gages on a Google map front end using NEXRAD (next generation weather radar) data is presented.

  • Virtual Sensors in a Web 2.0 Virtual Watershed
    2008 IEEE Fourth International Conference on eScience, 2008
    Co-Authors: Luigi Marini, Rob Kooper, Alejandro Rodriguez, David Hill, James Myers, Barbara Minsker
    Abstract:

    This paper presents a Web 2.0 virtual observatory framework applied in an environmental watershed research context, where users not only can access existing sensor data such as the USGS (United States Geological Survey) Rain Gage data in near real-time, but also can create and share virtual sensors and trigger their associated workflows on-the-fly. Categories of virtual sensors are discussed and community participation and collaboration on creating virtual sensors can be promoted. An eScience use case which allows users to create virtual Rain Gages on a Google map front end using NEXRAD (next generation weather radar) data is presented.

Witold F Krajewski - One of the best experts on this subject based on the ideXlab platform.

  • numerical simulation studies of Rain Gage data correction due to wind effect
    Journal of Geophysical Research, 1999
    Co-Authors: Emad Habib, Witold F Krajewski, Vladislav Nespor, Anton Kruger
    Abstract:

    Investigation of the correction of Rain Gage measurements due to the wind effect is described. The focus is on the effect of the temporal averaging scale on the estimation of the wind-induced error correction. Numerically derived correction formulae for a specific class of Rain Gage types, along with high temporal resolution measurements of Rainfall and wind speed, are used to perform the study. The Rainfall measurements are corrected on a variety of temporal scales ranging from 1 min to 1 month. The results showed the importance of applying the correction procedure at a short timescale, otherwise a significant overestimation of the wind-induced bias results. The wind-induced error is characterized by a nonlinear complex behavior dependent on wind speed, Rainfall rate, and the microphysical Rain structure quantified by a drop size distribution parameter. The estimated correction factors are found to be sensitive to the change of the drop size distribution. Finally, comparison with certain formulae reported in the literature showed a significant random scatter of the estimated correction if it is expressed only as a function of the wind speed and the drop size distribution characteristics are ignored.

  • Stochastic interpolation of Rainfall data from Rain Gages and radar using cokriging: 1. Design of experiments
    Water Resources Research, 1990
    Co-Authors: Witold F Krajewski, David S. Bowles
    Abstract:

    Cokriging is used to merge Rain Gage measurements and radar Rainfall data. The cokriging estimators included are ordinary, universal, and disjunctive. To evaluate the estimators, two simulation experiments are performed. The first experiment assumes that high-quality radar Rainfall fields are ground truth Rainfall fields. From each ground truth Rainfall field, multiple combinations of Rain Gage measurement field and radar Rainfall field are artificially generated with varying Gage network density and error characteristics of radar Rainfall. The second experiment uses a stochastic space-time Rainfall model to generate assumed ground truth Rainfall fields of various characteristics. Due to the sparsity of Rain Gage measurements, the second-order statistics required for cokriging can only be estimated with large uncertainty. The adverse effects of this uncertainty, and the point sampling error of Rain Gage measurements are explicitly assessed by cokriging the ground truth Rainfall data and the radar Rainfall data with near perfectly known second-order statistics.

  • Stochastic interpolation of Rainfall data from Rain Gages and radar using Cokriging: 2. Results
    Water Resources Research, 1990
    Co-Authors: Witold F Krajewski, Ali Azimi‐zonooz, David S. Bowles
    Abstract:

    Various estimation procedures using ordinary, universal, and disjunctive cokriging are evaluated in merging Rain Gage measurements and radar Rainfall data. The estimation procedures and the simulation experiments were described in part 1 (Seo et al., this issue) of this two-part work. In this part, the experiments are described in detail. An objective comparison scheme, devised to compare a large number of estimators, is also described. The results are presented focusing upon (1) the potential of radar-Gage estimation using cokriging over radar-only estimation and Gage-only estimation under widely varying conditions of Gage network density and the error characteristics of radar Rainfall, (2) the potential for using universal or disjunctive cokriging over ordinary cokriging, (3) how the uncertain second-order statistics affect the estimators, due to lack of Rain Gage measurements, and (4) how the statistical characteristics of ground truth Rainfall affect the estimators.